Welcome! In this interactive tutorial you will see how to use data science skills to analyses maternal and child health. Specifically you will be exploring birthweight and factors that can lead to low birthweight using the R statistical software.
Why Study Birthweight?
Birthweight is a crucial indicator of a newborn’s health and well-being. It serves as a fundamental metric in assessing a baby’s initial growth and development. Moreover, birthweight plays a pivotal role in predicting the infant’s short-term and long-term health outcomes. Babies born with low birthweight, typically defined as weighing less than 2,500 grams (5.5 pounds) at birth, face increased risks of complications, developmental issues, and chronic health conditions.
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Factors Influencing Low Birthweight
There are many factors that may contribute to low birthweight:
Maternal Nutrition: Adequate maternal nutrition is paramount for the proper growth and development of the fetus. Poor maternal nutrition, whether due to malnutrition or inadequate dietary intake, can result in low birthweight.
Maternal Health Conditions: Certain maternal health conditions, such as hypertension, diabetes, and infections, can impact fetal growth and contribute to low birthweight. Managing and treating these conditions during pregnancy is crucial for the well-being of both the mother and the baby.
Lifestyle Factors: Maternal lifestyle choices, including smoking, alcohol consumption, and illicit drug use, have been linked to low birthweight. These substances can negatively affect fetal development and increase the risk of complications.
Socioeconomic Factors: Socioeconomic status is a significant determinant of maternal and child health. Limited access to healthcare, education, and resources can contribute to low birthweight. Understanding these social determinants allows for targeted interventions to address disparities.
Multiple Pregnancies: Twins, triplets, or other multiple pregnancies are at a higher risk of low birthweight due to the shared resources in the womb.
Birthweight in context
In New South Wales, data on birthweight are routinely recorded in the Perinatal Data Collection, a population-based surveillance system covering all births in NSW public and private hospitals, as well as home births. It encompasses all live births, and stillbirths of at least 20 weeks gestation or at least 400 grams birthweight.
smoke Smoking status during pregnancy (0=No, 1 = Yes)
ht History of hypertension (0=No, 1 = Yes)
bwt Birthweight in grams.
birthwt |>select(age, smoke, ht, bwt) |>summary()
age smoke ht bwt
Min. :14.00 Min. :0.0000 Min. :0.00000 Min. : 709
1st Qu.:19.00 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:2414
Median :23.00 Median :0.0000 Median :0.00000 Median :2977
Mean :23.24 Mean :0.3915 Mean :0.06349 Mean :2945
3rd Qu.:26.00 3rd Qu.:1.0000 3rd Qu.:0.00000 3rd Qu.:3487
Max. :45.00 Max. :1.0000 Max. :1.00000 Max. :4990
For example, we can see that the median maternal age is 23 years and 39% of mums in this dataset smoked smoked during pregnancy.
Test your understanding
True or False? The oldest maternal age recorded was 40 years?
birthwt |>filter(age <=40) |>mutate(smokeCategorical =factor(smoke, labels =c('Non-smoker', 'Smoker') )) |>ggplot(aes(x = age, y = bwt, color = smokeCategorical, fill = smokeCategorical, shape = smokeCategorical)) +geom_point() +geom_smooth(method ='lm') +scale_x_continuous("Maternal age (years)") +scale_y_continuous("Birthweight (grams)", labels = scales::comma) +scale_shape_manual("Smoking status", values =c(21, 22)) +scale_color_manual("Smoking status", values =c('#03d77f', '#fb706a')) +scale_fill_manual("Smoking status", values =lighten(c('#03d77f', '#fb706a'), 0.4)) +labs(title="Birthweight by maternal age and maternal smoking status") +theme_minimal() +theme(legend.position ='top')
Test your understanding
Which R package provides tools for data visualisation?
Which statement is most accurate based on the figure above?
Exercise
Solution
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Statistical Modelling
model1 <-lm(bwt ~ smoke, data = birthwt)library(sjPlot)tab_model(model1, digits =0, title ='Birthweight')
Birthweight
bwt
Predictors
Estimates
CI
p
(Intercept)
3056
2924 – 3188
<0.001
smoke
-284
-495 – -73
0.009
Observations
189
R2 / R2 adjusted
0.036 / 0.031
We can interpret this as follows:
The average birthweight among babies born to non-smokers was 3,056 grams.
The 95% confidence interval (CI) for this estimate ranges from 2,294 grams to 3,188 grams. This is the range of values within which we are 95% confident that the true population coefficient lies. In other words, if you were to conduct the same study multiple times and calculate a 95% confidence interval for the coefficient for non-smokers each time, you would expect the true coefficient to fall within the range 2,294–3,188 grams in 95% of those intervals.
The average birthweight among babies born to smokers was 284 grams less than babies born to non-smokers.
The 95% confidence interval (CI) for this estimate ranges from -495 grams to -73 grams. This is the range of values within which we are 95% confident that the true population coefficient lies. In other words, if you were to conduct the same study multiple times and calculate a 95% confidence interval for the coefficient for smokers each time, you would expect the true coefficient to fall within the range -495—73 grams in 95% of those intervals.
Test your understanding
Birthweight
bwt
Predictors
Estimates
CI
p
(Intercept)
2791
2316 – 3267
<0.001
smoke
-278
-489 – -67
0.010
age
11
-8 – 31
0.255
Observations
189
R2 / R2 adjusted
0.043 / 0.033
The estimated coefficient for maternal age is
True or False? The 95% confidence interval for the coefficient of age includes 0
Footnotes
Ghimire, P.R.; Mooney, J.; Fox, L.; Dubois, L. Smoking Cessation during the Second Half of Pregnancy Prevents Low Birth Weight among Australian Born Babies in Regional New South Wales. Int. J. Environ. Res. Public Health 2021, 18, 3417. https://doi.org/10.3390/ijerph18073417↩︎